AI Medical Compendium Topic

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Bacteria

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New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning.

PLoS biology
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and stat...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...

Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

Journal of chemical information and modeling
While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine le...

Freeze-Thaw Imaging for Microorganism Classification Assisted with Artificial Intelligence.

ACS nano
Fast and cost-effective microbial classification is crucial for clinical diagnosis, environmental monitoring, and food safety. However, traditional methods encounter challenges including intricate procedures, skilled personnel needs, and sophisticate...

Deep learning enabled open-set bacteria recognition using surface-enhanced Raman spectroscopy.

Biosensors & bioelectronics
Accurate bacterial identification is vital in medical and healthcare settings. Traditional methods, though reliable, are often time-consuming, underscoring the need for faster, more efficient alternatives. Deep learning-assisted Surface-enhanced Rama...

Efficient detection of foodborne pathogens via SERS and deep learning: An ADMIN-optimized NAS-Unet approach.

Journal of hazardous materials
Amid the increasing global challenge of foodborne diseases, there is an urgent need for rapid and precise pathogen detection methods. This study innovatively integrates surface-enhanced Raman Spectroscopy (SERS) with deep learning technology to devel...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Environmental research
Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. In this study, we identified 33 different MPs features including their abundance, shape, color, size...

Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.

Microbial genomics
The prediction of the plasmid host range is crucial for investigating the dissemination of plasmids and the transfer of resistance and virulence genes mediated by plasmids. Several machine learning-based tools have been developed to predict plasmid h...

PharaCon: a new framework for identifying bacteriophages via conditional representation learning.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. Ho...

MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. There exist some plasmids that can transfer, replicate, or persist in multiple or...